A Note on Averaging Day-Ahead Electricity Price Forecasts Across Calibration Windows

被引：63
|
作者：
Hubicka, Katarzyna [1 ]
Marcjasz, Grzegorz [2 ]
Weron, Rafal [2 ]
机构：
[1] Wroclaw Univ Sci & Technol, Fac Pure & Appl Math, PL-50370 Wroclaw, Poland
[2] Wroclaw Univ Sci & Technol, Dept Operat Res, Fac Comp Sci & Management, PL-50370 Wroclaw, Poland
来源：
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY | 2019年 / 10卷 / 01期
关键词：
Electricity price forecasting; combining forecasts; calibration window; autoregression; NARX neural network; committee machine; Diebold-Mariano test;
D O I：
10.1109/TSTE.2018.2869557
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
We propose a novel concept in energy forecasting and show that averaging day-ahead electricity price forecasts of a predictive model across 28-728 day calibration windows yields better results than selecting only one "optimal" window length. Even more significant accuracy gains can be achieved by averaging over a few, carefully selected windows.
引用
收藏
页码：321 / 323
页数：3
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